INCOME

Atwater Village Income

## Will start with importing data
import pandas as pd

# We will begin by loading a data file for Atwater Village that details the structures built in the area.
# note the relative filepath! where is this file located?
df = pd.read_csv('data/Atwater2017_HHIncomeCategories.csv')

df = pd.read_csv(
    'data/Atwater2017_HHIncomeCategories.csv',
    dtype=
    {
        'Geo_FIPS':str,
        'Geo_STATE':str,
        'Geo_COUNTY': str
    }
)

df.columns[df.isna().all()].tolist()

['Geo_US',
 'Geo_REGION',
 'Geo_DIVISION',
 'Geo_STATECE',
 'Geo_COUSUB',
 'Geo_PLACE',
 'Geo_PLACESE',
 'Geo_CONCIT',
 'Geo_AIANHH',
 'Geo_AIANHHFP',
 'Geo_AIHHTLI',
 'Geo_AITSCE',
 'Geo_AITS',
 'Geo_ANRC',
 'Geo_CBSA',
 'Geo_CSA',
 'Geo_METDIV',
 'Geo_MACC',
 'Geo_MEMI',
 'Geo_NECTA',
 'Geo_CNECTA',
 'Geo_NECTADIV',
 'Geo_UA',
 'Geo_UACP',
 'Geo_CDCURR',
 'Geo_SLDU',
 'Geo_SLDL',
 'Geo_VTD',
 'Geo_ZCTA3',
 'Geo_ZCTA5',
 'Geo_SUBMCD',
 'Geo_SDELM',
 'Geo_SDSEC',
 'Geo_SDUNI',
 'Geo_UR',
 'Geo_PCI',
 'Geo_TAZ',
 'Geo_UGA',
 'Geo_BTTR',
 'Geo_BTBG',
 'Geo_PUMA5',
 'Geo_PUMA1']

df = df.dropna(axis=1,how="all")

df.head()

	Geo_FIPS	Geo_GEOID	Geo_NAME	Geo_QName	Geo_STUSAB	Geo_SUMLEV	Geo_GEOCOMP	Geo_FILEID	Geo_LOGRECNO	Geo_STATE	Geo_COUNTY	Geo_TRACT	Geo_BLKGRP	SE_B14001_001	SE_B14001_002	SE_B14001_003	SE_B14001_004	SE_B14001_005	SE_B14001_006
0	060371863011	15000US060371863011	Block Group 1, Census Tract 1863.01, Los Angel...	Block Group 1, Census Tract 1863.01, Los Angel...	ca	150	0	ACSSF	15232	06	037	186301	1	330	46	67	54	50	113
1	060371863012	15000US060371863012	Block Group 2, Census Tract 1863.01, Los Angel...	Block Group 2, Census Tract 1863.01, Los Angel...	ca	150	0	ACSSF	15233	06	037	186301	2	327	95	148	31	9	44
2	060371863013	15000US060371863013	Block Group 3, Census Tract 1863.01, Los Angel...	Block Group 3, Census Tract 1863.01, Los Angel...	ca	150	0	ACSSF	15234	06	037	186301	3	268	73	129	44	10	12
3	060371863021	15000US060371863021	Block Group 1, Census Tract 1863.02, Los Angel...	Block Group 1, Census Tract 1863.02, Los Angel...	ca	150	0	ACSSF	15235	06	037	186302	1	584	129	115	109	71	160
4	060371864011	15000US060371864011	Block Group 1, Census Tract 1864.01, Los Angel...	Block Group 1, Census Tract 1864.01, Los Angel...	ca	150	0	ACSSF	15238	06	037	186401	1	675	199	167	172	69	68

# list of additional columns to drop
columns_to_drop = ['Geo_GEOID','Geo_STUSAB','Geo_SUMLEV','Geo_GEOCOMP','Geo_FILEID','Geo_LOGRECNO','Geo_STATE','Geo_COUNTY','Geo_TRACT']

# next, drop them!
df = df.drop(columns_to_drop,axis=1)
df.head()

	Geo_FIPS	Geo_NAME	Geo_QName	Geo_BLKGRP	SE_B14001_001	SE_B14001_002	SE_B14001_003	SE_B14001_004	SE_B14001_005	SE_B14001_006
0	060371863011	Block Group 1, Census Tract 1863.01, Los Angel...	Block Group 1, Census Tract 1863.01, Los Angel...	1	330	46	67	54	50	113
1	060371863012	Block Group 2, Census Tract 1863.01, Los Angel...	Block Group 2, Census Tract 1863.01, Los Angel...	2	327	95	148	31	9	44
2	060371863013	Block Group 3, Census Tract 1863.01, Los Angel...	Block Group 3, Census Tract 1863.01, Los Angel...	3	268	73	129	44	10	12
3	060371863021	Block Group 1, Census Tract 1863.02, Los Angel...	Block Group 1, Census Tract 1863.02, Los Angel...	1	584	129	115	109	71	160
4	060371864011	Block Group 1, Census Tract 1864.01, Los Angel...	Block Group 1, Census Tract 1864.01, Los Angel...	1	675	199	167	172	69	68

df.columns = ['FIPS',
'Geo_NAME',
 'Geo_QName',
 'Geo_BLKGRP',
 'Total Households in Atwater Village',
 'Household Income Less than $25,000',
 'Household Income $25,000 to $49,999',
 'Household Income $50,000 to $74,999',
 'Household Income $75,000 to $99,999',
 'Household Income $100,000 or More']

df.head()

	FIPS	Geo_NAME	Geo_QName	Geo_BLKGRP	Total Households in Atwater Village	Household Income Less than $25,000	Household Income $25,000 to $49,999	Household Income $50,000 to $74,999	Household Income $75,000 to $99,999	Household Income $100,000 or More
0	060371863011	Block Group 1, Census Tract 1863.01, Los Angel...	Block Group 1, Census Tract 1863.01, Los Angel...	1	330	46	67	54	50	113
1	060371863012	Block Group 2, Census Tract 1863.01, Los Angel...	Block Group 2, Census Tract 1863.01, Los Angel...	2	327	95	148	31	9	44
2	060371863013	Block Group 3, Census Tract 1863.01, Los Angel...	Block Group 3, Census Tract 1863.01, Los Angel...	3	268	73	129	44	10	12
3	060371863021	Block Group 1, Census Tract 1863.02, Los Angel...	Block Group 1, Census Tract 1863.02, Los Angel...	1	584	129	115	109	71	160
4	060371864011	Block Group 1, Census Tract 1864.01, Los Angel...	Block Group 1, Census Tract 1864.01, Los Angel...	1	675	199	167	172	69	68

Clean Up Data for Bar Graphs (Atwater)

df_sum= df.sum().reset_index()

df_sum.head()

	index	0
0	FIPS	0603718630110603718630120603718630130603718630...
1	Geo_NAME	Block Group 1, Census Tract 1863.01, Los Angel...
2	Geo_QName	Block Group 1, Census Tract 1863.01, Los Angel...
3	Geo_BLKGRP	31
4	Total Households in Atwater Village	8748

df_sum=df_sum.set_index('index').T

df_sum.head()

index	FIPS	Geo_NAME	Geo_QName	Geo_BLKGRP	Total Households in Atwater Village	Household Income Less than $25,000	Household Income $25,000 to $49,999	Household Income $50,000 to $74,999	Household Income $75,000 to $99,999	Household Income $100,000 or More
0	0603718630110603718630120603718630130603718630...	Block Group 1, Census Tract 1863.01, Los Angel...	Block Group 1, Census Tract 1863.01, Los Angel...	31	8748	1997	1879	1339	1157	2376

# create a new column and normalize for SUMA 
# also repeat this for 'p_more_100k'
df_sum['% Less than $25K'] = df_sum['Household Income Less than $25,000']/df_sum['Total Households in Atwater Village']*100

df_sum['% More than $100K'] = df_sum['Household Income $100,000 or More']/df_sum['Total Households in Atwater Village']*100

df_sum['neighborhood'] = 'Atwater'
df_sum['year'] = '2017'

df_sum.plot.bar(x = 'neighborhood', y='% Less than $25K', title="Percentage of Households with Income Less than $25K (2017)")

<matplotlib.axes._subplots.AxesSubplot at 0x7f3dd4287670>

df_sum.plot.bar(x = 'neighborhood', y='% More than $100K', title="Percentage of Households with Income More than $100K (2017)")

<matplotlib.axes._subplots.AxesSubplot at 0x7f3dd42bcd30>

type(df_sum)

pandas.core.frame.DataFrame

Normalize Data for Mapping

df['Total Households in Atwater Village'].head()

0    330
1    327
2    268
3    584
4    675
Name: Total Households in Atwater Village, dtype: int64

# create a column to define the neighborhood
df['neighborhood'] = 'Atwater'

# create a new column and normalize
# also repeat this for 'p_more_100k'
df['p_less_25k'] = df['Household Income Less than $25,000']/df['Total Households in Atwater Village']*100

df['p_more_100k'] = df['Household Income $100,000 or More']/df['Total Households in Atwater Village']*100

df['Total Households'] = df ['Total Households in Atwater Village']

df.head()

	FIPS	Geo_NAME	Geo_QName	Geo_BLKGRP	Total Households in Atwater Village	Household Income Less than $25,000	Household Income $25,000 to $49,999	Household Income $50,000 to $74,999	Household Income $75,000 to $99,999	Household Income $100,000 or More	neighborhood	p_less_25k	p_more_100k	Total Households
0	060371863011	Block Group 1, Census Tract 1863.01, Los Angel...	Block Group 1, Census Tract 1863.01, Los Angel...	1	330	46	67	54	50	113	Atwater	13.939394	34.242424	330
1	060371863012	Block Group 2, Census Tract 1863.01, Los Angel...	Block Group 2, Census Tract 1863.01, Los Angel...	2	327	95	148	31	9	44	Atwater	29.051988	13.455657	327
2	060371863013	Block Group 3, Census Tract 1863.01, Los Angel...	Block Group 3, Census Tract 1863.01, Los Angel...	3	268	73	129	44	10	12	Atwater	27.238806	4.477612	268
3	060371863021	Block Group 1, Census Tract 1863.02, Los Angel...	Block Group 1, Census Tract 1863.02, Los Angel...	1	584	129	115	109	71	160	Atwater	22.089041	27.397260	584
4	060371864011	Block Group 1, Census Tract 1864.01, Los Angel...	Block Group 1, Census Tract 1864.01, Los Angel...	1	675	199	167	172	69	68	Atwater	29.481481	10.074074	675

import plotly.express as px
import pandas as pd

px.bar(df, x="neighborhood", y=["Household Income Less than $25,000",
 "Household Income $25,000 to $49,999",
 "Household Income $50,000 to $74,999",
 "Household Income $75,000 to $99,999",
 "Household Income $100,000 or More"], title="Income Breakdown by Block Group in Atwater Village (2017)", 
       labels={'Geo_NAME':'Census Tract Block Group','value':'Total Households in Atwater Village', 'variable': 'Household Income'})

Leimert Park Income

df2 = pd.read_csv('data/Leimert2017_HHIncomeCategories.csv')

df2 = pd.read_csv(
    'data/Leimert2017_HHIncomeCategories.csv',
    dtype=
    {
        'Geo_FIPS':str,
        'Geo_STATE':str,
        'Geo_COUNTY': str
    }
)

df2.columns[df2.isna().all()].tolist()

['Geo_US',
 'Geo_REGION',
 'Geo_DIVISION',
 'Geo_STATECE',
 'Geo_COUSUB',
 'Geo_PLACE',
 'Geo_PLACESE',
 'Geo_CONCIT',
 'Geo_AIANHH',
 'Geo_AIANHHFP',
 'Geo_AIHHTLI',
 'Geo_AITSCE',
 'Geo_AITS',
 'Geo_ANRC',
 'Geo_CBSA',
 'Geo_CSA',
 'Geo_METDIV',
 'Geo_MACC',
 'Geo_MEMI',
 'Geo_NECTA',
 'Geo_CNECTA',
 'Geo_NECTADIV',
 'Geo_UA',
 'Geo_UACP',
 'Geo_CDCURR',
 'Geo_SLDU',
 'Geo_SLDL',
 'Geo_VTD',
 'Geo_ZCTA3',
 'Geo_ZCTA5',
 'Geo_SUBMCD',
 'Geo_SDELM',
 'Geo_SDSEC',
 'Geo_SDUNI',
 'Geo_UR',
 'Geo_PCI',
 'Geo_TAZ',
 'Geo_UGA',
 'Geo_BTTR',
 'Geo_BTBG',
 'Geo_PUMA5',
 'Geo_PUMA1']

df2 = df2.dropna(axis=1,how="all")

df2.head()

	Geo_FIPS	Geo_GEOID	Geo_NAME	Geo_QName	Geo_STUSAB	Geo_SUMLEV	Geo_GEOCOMP	Geo_FILEID	Geo_LOGRECNO	Geo_STATE	Geo_COUNTY	Geo_TRACT	Geo_BLKGRP	SE_B14001_001	SE_B14001_002	SE_B14001_003	SE_B14001_004	SE_B14001_005	SE_B14001_006
0	060372190203	15000US060372190203	Block Group 3, Census Tract 2190.20, Los Angel...	Block Group 3, Census Tract 2190.20, Los Angel...	ca	150	0	ACSSF	15883	06	037	219020	3	370	167	46	115	33	9
1	060372340001	15000US060372340001	Block Group 1, Census Tract 2340, Los Angeles ...	Block Group 1, Census Tract 2340, Los Angeles ...	ca	150	0	ACSSF	16104	06	037	234000	1	634	147	140	146	56	145
2	060372340002	15000US060372340002	Block Group 2, Census Tract 2340, Los Angeles ...	Block Group 2, Census Tract 2340, Los Angeles ...	ca	150	0	ACSSF	16105	06	037	234000	2	267	44	58	22	14	129
3	060372340003	15000US060372340003	Block Group 3, Census Tract 2340, Los Angeles ...	Block Group 3, Census Tract 2340, Los Angeles ...	ca	150	0	ACSSF	16106	06	037	234000	3	247	14	47	28	25	133
4	060372340004	15000US060372340004	Block Group 4, Census Tract 2340, Los Angeles ...	Block Group 4, Census Tract 2340, Los Angeles ...	ca	150	0	ACSSF	16107	06	037	234000	4	617	219	71	117	112	98

df2 = df2.drop(columns_to_drop,axis=1)
df2.head()

	Geo_FIPS	Geo_NAME	Geo_QName	Geo_BLKGRP	SE_B14001_001	SE_B14001_002	SE_B14001_003	SE_B14001_004	SE_B14001_005	SE_B14001_006
0	060372190203	Block Group 3, Census Tract 2190.20, Los Angel...	Block Group 3, Census Tract 2190.20, Los Angel...	3	370	167	46	115	33	9
1	060372340001	Block Group 1, Census Tract 2340, Los Angeles ...	Block Group 1, Census Tract 2340, Los Angeles ...	1	634	147	140	146	56	145
2	060372340002	Block Group 2, Census Tract 2340, Los Angeles ...	Block Group 2, Census Tract 2340, Los Angeles ...	2	267	44	58	22	14	129
3	060372340003	Block Group 3, Census Tract 2340, Los Angeles ...	Block Group 3, Census Tract 2340, Los Angeles ...	3	247	14	47	28	25	133
4	060372340004	Block Group 4, Census Tract 2340, Los Angeles ...	Block Group 4, Census Tract 2340, Los Angeles ...	4	617	219	71	117	112	98

df2.columns = ['FIPS',
'Geo_NAME',
 'Geo_QName',
 'Geo_BLKGRP',
 'Total Households in Leimert Park',
 'Household Income Less than $25,000',
 'Household Income $25,000 to $49,999',
 'Household Income $50,000 to $74,999',
 'Household Income $75,000 to $99,999',
 'Household Income $100,000 or More']

df2.head()

	FIPS	Geo_NAME	Geo_QName	Geo_BLKGRP	Total Households in Leimert Park	Household Income Less than $25,000	Household Income $25,000 to $49,999	Household Income $50,000 to $74,999	Household Income $75,000 to $99,999	Household Income $100,000 or More
0	060372190203	Block Group 3, Census Tract 2190.20, Los Angel...	Block Group 3, Census Tract 2190.20, Los Angel...	3	370	167	46	115	33	9
1	060372340001	Block Group 1, Census Tract 2340, Los Angeles ...	Block Group 1, Census Tract 2340, Los Angeles ...	1	634	147	140	146	56	145
2	060372340002	Block Group 2, Census Tract 2340, Los Angeles ...	Block Group 2, Census Tract 2340, Los Angeles ...	2	267	44	58	22	14	129
3	060372340003	Block Group 3, Census Tract 2340, Los Angeles ...	Block Group 3, Census Tract 2340, Los Angeles ...	3	247	14	47	28	25	133
4	060372340004	Block Group 4, Census Tract 2340, Los Angeles ...	Block Group 4, Census Tract 2340, Los Angeles ...	4	617	219	71	117	112	98

Clean Up Data for Bar Graphs (Leimert)

df2_sum= df2.sum().reset_index()

df2_sum=df2_sum.set_index('index').T

df2_sum.head()

index	FIPS	Geo_NAME	Geo_QName	Geo_BLKGRP	Total Households in Leimert Park	Household Income Less than $25,000	Household Income $25,000 to $49,999	Household Income $50,000 to $74,999	Household Income $75,000 to $99,999	Household Income $100,000 or More
0	0603721902030603723400010603723400020603723400...	Block Group 3, Census Tract 2190.20, Los Angel...	Block Group 3, Census Tract 2190.20, Los Angel...	34	5865	1885	1169	1051	560	1200

df2_sum['% Less than $25K'] = df2_sum['Household Income Less than $25,000']/df2_sum['Total Households in Leimert Park']*100

df2_sum['% More than $100K'] = df2_sum['Household Income $100,000 or More']/df2_sum['Total Households in Leimert Park']*100

df2_sum['neighborhood'] = 'Leimert'
df2_sum['year'] = '2017'

df2_sum.head()

index	FIPS	Geo_NAME	Geo_QName	Geo_BLKGRP	Total Households in Leimert Park	Household Income Less than $25,000	Household Income $25,000 to $49,999	Household Income $50,000 to $74,999	Household Income $75,000 to $99,999	Household Income $100,000 or More	% Less than $25K	% More than $100K	neighborhood	year
0	0603721902030603723400010603723400020603723400...	Block Group 3, Census Tract 2190.20, Los Angel...	Block Group 3, Census Tract 2190.20, Los Angel...	34	5865	1885	1169	1051	560	1200	32.1398	20.4604	Leimert	2017

Normalize Data for Mapping

# create a new column and normalize
# also repeat this for 'p_more_100k'
df2['p_less_25k'] = df2['Household Income Less than $25,000']/df2['Total Households in Leimert Park']*100

# create a column to define the neighborhood
df2['neighborhood'] = 'Leimert'

df2['p_more_100k'] = df2['Household Income $100,000 or More']/df2['Total Households in Leimert Park']*100

df2['Total Households'] = df2['Total Households in Leimert Park']

px.bar(df2, x="neighborhood", y=["Household Income Less than $25,000",
 "Household Income $25,000 to $49,999",
 "Household Income $50,000 to $74,999",
 "Household Income $75,000 to $99,999",
 "Household Income $100,000 or More"], title="Income Breakdown by Block Group in Leimert Park (2017)", 
       labels={'Geo_NAME':'Census Tract Block Group','value':'Total Households in Leimert Park', 'variable': 'Household Income'})

income_merged= df.append(df2)

incomefig2017 = px.bar(income_merged, x="neighborhood", y=["Household Income Less than $25,000",
 "Household Income $25,000 to $49,999",
 "Household Income $50,000 to $74,999",
 "Household Income $75,000 to $99,999",
 "Household Income $100,000 or More"], title="Income Breakdown by Neighborhood (2017)", 
       labels={'Geo_NAME':'Census Tract Block Group','value':'Total Households in Atwater Village & Leimert Park', 'variable': 'Household Income'})

incomefig2017.update_traces(marker_line_width=0)

incomefig2017.show()

incomefig2017.write_html("income2017_stacked_plotly.html")

Merge Atwater Village + Leimert Park Income Data

income_merged= df_sum.append(df2_sum)

income_merged

	FIPS	Geo_NAME	Geo_QName	Geo_BLKGRP	Total Households in Atwater Village	Household Income Less than $25,000	Household Income $25,000 to $49,999	Household Income $50,000 to $74,999	Household Income $75,000 to $99,999	Household Income $100,000 or More	% Less than $25K	% More than $100K	neighborhood	year	Total Households in Leimert Park
0	0603718630110603718630120603718630130603718630...	Block Group 1, Census Tract 1863.01, Los Angel...	Block Group 1, Census Tract 1863.01, Los Angel...	31	8748	1997	1879	1339	1157	2376	22.8281	27.1605	Atwater	2017	NaN
0	0603721902030603723400010603723400020603723400...	Block Group 3, Census Tract 2190.20, Los Angel...	Block Group 3, Census Tract 2190.20, Los Angel...	34	NaN	1885	1169	1051	560	1200	32.1398	20.4604	Leimert	2017	5865

income_merged.plot.bar(x = 'neighborhood', y='% Less than $25K', title="Percentage of Households with Income Less than $25K (2017)")

<matplotlib.axes._subplots.AxesSubplot at 0x7f3dd1d18130>

income_merged.plot.bar(x = 'neighborhood', y='% More than $100K', title="Percentage of Households with Income More than $100K (2017)")

<matplotlib.axes._subplots.AxesSubplot at 0x7f3dd1cd27f0>

RACE

Atwater Village Race Data

df3 = pd.read_csv('data/Atwater2017_Race.csv')

df3 = pd.read_csv(
    'data/Atwater2017_Race.csv',
    dtype=
    {
        'Geo_FIPS':str,
        'Geo_STATE':str,
        'Geo_COUNTY': str
    }
)

df3.columns[df3.isna().all()].tolist()

['Geo_US',
 'Geo_REGION',
 'Geo_DIVISION',
 'Geo_STATECE',
 'Geo_COUSUB',
 'Geo_PLACE',
 'Geo_PLACESE',
 'Geo_CONCIT',
 'Geo_AIANHH',
 'Geo_AIANHHFP',
 'Geo_AIHHTLI',
 'Geo_AITSCE',
 'Geo_AITS',
 'Geo_ANRC',
 'Geo_CBSA',
 'Geo_CSA',
 'Geo_METDIV',
 'Geo_MACC',
 'Geo_MEMI',
 'Geo_NECTA',
 'Geo_CNECTA',
 'Geo_NECTADIV',
 'Geo_UA',
 'Geo_UACP',
 'Geo_CDCURR',
 'Geo_SLDU',
 'Geo_SLDL',
 'Geo_VTD',
 'Geo_ZCTA3',
 'Geo_ZCTA5',
 'Geo_SUBMCD',
 'Geo_SDELM',
 'Geo_SDSEC',
 'Geo_SDUNI',
 'Geo_UR',
 'Geo_PCI',
 'Geo_TAZ',
 'Geo_UGA',
 'Geo_BTTR',
 'Geo_BTBG',
 'Geo_PUMA5',
 'Geo_PUMA1']

df3 = df3.dropna(axis=1,how="all")

columns_to_drop = ['Geo_GEOID','Geo_STUSAB','Geo_SUMLEV','Geo_GEOCOMP','Geo_FILEID','Geo_LOGRECNO','Geo_STATE', 'Geo_COUNTY','SE_A04001_002','SE_A04001_011','SE_A04001_012','SE_A04001_013','SE_A04001_014','SE_A04001_015','SE_A04001_016','SE_A04001_017']

df3 = df3.drop(columns_to_drop,axis=1)
df3.head()

	Geo_FIPS	Geo_NAME	Geo_QName	Geo_TRACT	Geo_BLKGRP	SE_A04001_001	SE_A04001_003	SE_A04001_004	SE_A04001_005	SE_A04001_006	SE_A04001_007	SE_A04001_008	SE_A04001_009	SE_A04001_010
0	060371863011	Block Group 1, Census Tract 1863.01, Los Angel...	Block Group 1, Census Tract 1863.01, Los Angel...	186301	1	881	322	53	0	78	0	0	44	384
1	060371863012	Block Group 2, Census Tract 1863.01, Los Angel...	Block Group 2, Census Tract 1863.01, Los Angel...	186301	2	1349	103	23	0	89	0	0	6	1128
2	060371863013	Block Group 3, Census Tract 1863.01, Los Angel...	Block Group 3, Census Tract 1863.01, Los Angel...	186301	3	889	59	25	0	196	0	0	4	605
3	060371863021	Block Group 1, Census Tract 1863.02, Los Angel...	Block Group 1, Census Tract 1863.02, Los Angel...	186302	1	1573	566	26	0	409	0	0	84	488
4	060371864011	Block Group 1, Census Tract 1864.01, Los Angel...	Block Group 1, Census Tract 1864.01, Los Angel...	186401	1	2676	78	0	0	312	0	0	7	2279

columns3 = list(df3) # this is the same as df.columns.to_list()
columns3

['Geo_FIPS',
 'Geo_NAME',
 'Geo_QName',
 'Geo_TRACT',
 'Geo_BLKGRP',
 'SE_A04001_001',
 'SE_A04001_003',
 'SE_A04001_004',
 'SE_A04001_005',
 'SE_A04001_006',
 'SE_A04001_007',
 'SE_A04001_008',
 'SE_A04001_009',
 'SE_A04001_010']

##RENAMED TO INCLUDE PROPER TITLES 

df3.columns = ['FIPS',
 'Geo_NAME',
 'Geo_QName',
 'Geo_TRACT',
 'Geo_BLKGRP',
 'Total Population',
 'White',
 'Black',
 'AmIndian',
'Native Hawaiian and Other Pacific Islander Alone',
 'Asian',
 'Some Other Race Alone',
 'Two or More Races',
 'Hispanic or Latino']

df3.head()

	FIPS	Geo_NAME	Geo_QName	Geo_TRACT	Geo_BLKGRP	Total Population	White	Black	AmIndian	Native Hawaiian and Other Pacific Islander Alone	Asian	Some Other Race Alone	Two or More Races	Hispanic or Latino
0	060371863011	Block Group 1, Census Tract 1863.01, Los Angel...	Block Group 1, Census Tract 1863.01, Los Angel...	186301	1	881	322	53	0	78	0	0	44	384
1	060371863012	Block Group 2, Census Tract 1863.01, Los Angel...	Block Group 2, Census Tract 1863.01, Los Angel...	186301	2	1349	103	23	0	89	0	0	6	1128
2	060371863013	Block Group 3, Census Tract 1863.01, Los Angel...	Block Group 3, Census Tract 1863.01, Los Angel...	186301	3	889	59	25	0	196	0	0	4	605
3	060371863021	Block Group 1, Census Tract 1863.02, Los Angel...	Block Group 1, Census Tract 1863.02, Los Angel...	186302	1	1573	566	26	0	409	0	0	84	488
4	060371864011	Block Group 1, Census Tract 1864.01, Los Angel...	Block Group 1, Census Tract 1864.01, Los Angel...	186401	1	2676	78	0	0	312	0	0	7	2279

df3_sum= df3.sum().reset_index()

df3_sum

	index	0
0	FIPS	0603718630110603718630120603718630130603718630...
1	Geo_NAME	Block Group 1, Census Tract 1863.01, Los Angel...
2	Geo_QName	Block Group 1, Census Tract 1863.01, Los Angel...
3	Geo_TRACT	3368419
4	Geo_BLKGRP	31
5	Total Population	25387
6	White	5639
7	Black	270
8	AmIndian	157
9	Native Hawaiian and Other Pacific Islander Alone	4663
10	Asian	66
11	Some Other Race Alone	48
12	Two or More Races	721
13	Hispanic or Latino	13823

df3_sum = df3_sum.set_index('index').T

type(df3_sum)

pandas.core.frame.DataFrame

df3_sum.head()

index	FIPS	Geo_NAME	Geo_QName	Geo_TRACT	Geo_BLKGRP	Total Population	White	Black	AmIndian	Native Hawaiian and Other Pacific Islander Alone	Asian	Some Other Race Alone	Two or More Races	Hispanic or Latino
0	0603718630110603718630120603718630130603718630...	Block Group 1, Census Tract 1863.01, Los Angel...	Block Group 1, Census Tract 1863.01, Los Angel...	3368419	31	25387	5639	270	157	4663	66	48	721	13823

df3_sum['% Latino'] = df3_sum['Hispanic or Latino']/df3_sum['Total Population']*100

df3_sum['% White'] = df3_sum['White']/df3_sum['Total Population']*100

df3_sum['% Black'] = df3_sum['Black']/df3_sum['Total Population']*100

df3_sum['neighborhood'] = 'Atwater'
df3_sum['year'] = '2017'

df3_sum.head()

index	FIPS	Geo_NAME	Geo_QName	Geo_TRACT	Geo_BLKGRP	Total Population	White	Black	AmIndian	Native Hawaiian and Other Pacific Islander Alone	Asian	Some Other Race Alone	Two or More Races	Hispanic or Latino	% Latino	% White	% Black	neighborhood	year
0	0603718630110603718630120603718630130603718630...	Block Group 1, Census Tract 1863.01, Los Angel...	Block Group 1, Census Tract 1863.01, Los Angel...	3368419	31	25387	5639	270	157	4663	66	48	721	13823	54.4491	22.2122	1.06354	Atwater	2017

Leimert Park Race Data

df4 = pd.read_csv('data/Leimert2017_Race.csv')

df4 = pd.read_csv(
    'data/Leimert2017_Race.csv',
    dtype=
    {
        'Geo_FIPS':str,
        'Geo_STATE':str,
        'Geo_COUNTY': str
    }
)

df4.columns[df4.isna().all()].tolist()

['Geo_US',
 'Geo_REGION',
 'Geo_DIVISION',
 'Geo_STATECE',
 'Geo_COUSUB',
 'Geo_PLACE',
 'Geo_PLACESE',
 'Geo_CONCIT',
 'Geo_AIANHH',
 'Geo_AIANHHFP',
 'Geo_AIHHTLI',
 'Geo_AITSCE',
 'Geo_AITS',
 'Geo_ANRC',
 'Geo_CBSA',
 'Geo_CSA',
 'Geo_METDIV',
 'Geo_MACC',
 'Geo_MEMI',
 'Geo_NECTA',
 'Geo_CNECTA',
 'Geo_NECTADIV',
 'Geo_UA',
 'Geo_UACP',
 'Geo_CDCURR',
 'Geo_SLDU',
 'Geo_SLDL',
 'Geo_VTD',
 'Geo_ZCTA3',
 'Geo_ZCTA5',
 'Geo_SUBMCD',
 'Geo_SDELM',
 'Geo_SDSEC',
 'Geo_SDUNI',
 'Geo_UR',
 'Geo_PCI',
 'Geo_TAZ',
 'Geo_UGA',
 'Geo_BTTR',
 'Geo_BTBG',
 'Geo_PUMA5',
 'Geo_PUMA1']

df4 = df4.dropna(axis=1,how="all")

columns_to_drop = ['Geo_GEOID','Geo_STUSAB','Geo_SUMLEV','Geo_GEOCOMP','Geo_FILEID','Geo_LOGRECNO','Geo_STATE', 'Geo_COUNTY','SE_A04001_002','SE_A04001_011','SE_A04001_012','SE_A04001_013','SE_A04001_014','SE_A04001_015','SE_A04001_016','SE_A04001_017']

df4 = df4.drop(columns_to_drop,axis=1)
df4.head()

	Geo_FIPS	Geo_NAME	Geo_QName	Geo_TRACT	Geo_BLKGRP	SE_A04001_001	SE_A04001_003	SE_A04001_004	SE_A04001_005	SE_A04001_006	SE_A04001_007	SE_A04001_008	SE_A04001_009	SE_A04001_010
0	060372190203	Block Group 3, Census Tract 2190.20, Los Angel...	Block Group 3, Census Tract 2190.20, Los Angel...	219020	3	1074	10	480	0	33	0	0	0	551
1	060372340001	Block Group 1, Census Tract 2340, Los Angeles ...	Block Group 1, Census Tract 2340, Los Angeles ...	234000	1	1849	7	849	0	0	0	0	45	948
2	060372340002	Block Group 2, Census Tract 2340, Los Angeles ...	Block Group 2, Census Tract 2340, Los Angeles ...	234000	2	559	59	428	0	9	0	0	11	52
3	060372340003	Block Group 3, Census Tract 2340, Los Angeles ...	Block Group 3, Census Tract 2340, Los Angeles ...	234000	3	697	41	606	0	23	0	0	19	8
4	060372340004	Block Group 4, Census Tract 2340, Los Angeles ...	Block Group 4, Census Tract 2340, Los Angeles ...	234000	4	1267	128	852	0	6	0	0	45	236

columns4 = list(df4) # this is the same as df.columns.to_list()
columns4

['Geo_FIPS',
 'Geo_NAME',
 'Geo_QName',
 'Geo_TRACT',
 'Geo_BLKGRP',
 'SE_A04001_001',
 'SE_A04001_003',
 'SE_A04001_004',
 'SE_A04001_005',
 'SE_A04001_006',
 'SE_A04001_007',
 'SE_A04001_008',
 'SE_A04001_009',
 'SE_A04001_010']

df4.columns = ['FIPS',
 'Geo_NAME',
 'Geo_QName',
 'Geo_TRACT',
 'Geo_BLKGRP',
 'Total Population',
 'White',
 'Black',
 'AmIndian',
'Native Hawaiian and Other Pacific Islander Alone',
 'Asian',
 'Some Other Race Alone',
 'Two or More Races',
 'Hispanic or Latino']

df4.head()

	FIPS	Geo_NAME	Geo_QName	Geo_TRACT	Geo_BLKGRP	Total Population	White	Black	AmIndian	Native Hawaiian and Other Pacific Islander Alone	Asian	Some Other Race Alone	Two or More Races	Hispanic or Latino
0	060372190203	Block Group 3, Census Tract 2190.20, Los Angel...	Block Group 3, Census Tract 2190.20, Los Angel...	219020	3	1074	10	480	0	33	0	0	0	551
1	060372340001	Block Group 1, Census Tract 2340, Los Angeles ...	Block Group 1, Census Tract 2340, Los Angeles ...	234000	1	1849	7	849	0	0	0	0	45	948
2	060372340002	Block Group 2, Census Tract 2340, Los Angeles ...	Block Group 2, Census Tract 2340, Los Angeles ...	234000	2	559	59	428	0	9	0	0	11	52
3	060372340003	Block Group 3, Census Tract 2340, Los Angeles ...	Block Group 3, Census Tract 2340, Los Angeles ...	234000	3	697	41	606	0	23	0	0	19	8
4	060372340004	Block Group 4, Census Tract 2340, Los Angeles ...	Block Group 4, Census Tract 2340, Los Angeles ...	234000	4	1267	128	852	0	6	0	0	45	236

df4_sum= df4.sum().reset_index()

df4_sum=df4_sum.set_index('index').T

type(df4_sum)

pandas.core.frame.DataFrame

df4_sum.head()

index	FIPS	Geo_NAME	Geo_QName	Geo_TRACT	Geo_BLKGRP	Total Population	White	Black	AmIndian	Native Hawaiian and Other Pacific Islander Alone	Asian	Some Other Race Alone	Two or More Races	Hispanic or Latino
0	0603721902030603723400010603723400020603723400...	Block Group 3, Census Tract 2190.20, Los Angel...	Block Group 3, Census Tract 2190.20, Los Angel...	3028820	34	12623	552	8641	0	412	9	10	425	2574

df4_sum['% Black'] = (df4_sum['Black']/df4_sum['Total Population'])*100

df4_sum['% White'] = (df4_sum['White']/df4_sum['Total Population'])*100

df4_sum['% Latino'] = (df4_sum['Hispanic or Latino']/df4_sum['Total Population'])*100

df4_sum.head()

index	FIPS	Geo_NAME	Geo_QName	Geo_TRACT	Geo_BLKGRP	Total Population	White	Black	AmIndian	Native Hawaiian and Other Pacific Islander Alone	Asian	Some Other Race Alone	Two or More Races	Hispanic or Latino	% Black	% White	% Latino
0	0603721902030603723400010603723400020603723400...	Block Group 3, Census Tract 2190.20, Los Angel...	Block Group 3, Census Tract 2190.20, Los Angel...	3028820	34	12623	552	8641	0	412	9	10	425	2574	68.4544	4.37297	20.3913

df4_sum['neighborhood'] = 'Leimert'
df4_sum['year'] = '2017'

df4_sum

index	FIPS	Geo_NAME	Geo_QName	Geo_TRACT	Geo_BLKGRP	Total Population	White	Black	AmIndian	Native Hawaiian and Other Pacific Islander Alone	Asian	Some Other Race Alone	Two or More Races	Hispanic or Latino	% Black	% White	% Latino	neighborhood	year
0	0603721902030603723400010603723400020603723400...	Block Group 3, Census Tract 2190.20, Los Angel...	Block Group 3, Census Tract 2190.20, Los Angel...	3028820	34	12623	552	8641	0	412	9	10	425	2574	68.4544	4.37297	20.3913	Leimert	2017

race_merged= df3_sum.append(df4_sum)

race_merged

	FIPS	Geo_NAME	Geo_QName	Geo_TRACT	Geo_BLKGRP	Total Population	White	Black	AmIndian	Native Hawaiian and Other Pacific Islander Alone	Asian	Some Other Race Alone	Two or More Races	Hispanic or Latino	% Latino	% White	% Black	neighborhood	year
0	0603718630110603718630120603718630130603718630...	Block Group 1, Census Tract 1863.01, Los Angel...	Block Group 1, Census Tract 1863.01, Los Angel...	3368419	31	25387	5639	270	157	4663	66	48	721	13823	54.4491	22.2122	1.06354	Atwater	2017
0	0603721902030603723400010603723400020603723400...	Block Group 3, Census Tract 2190.20, Los Angel...	Block Group 3, Census Tract 2190.20, Los Angel...	3028820	34	12623	552	8641	0	412	9	10	425	2574	20.3913	4.37297	68.4544	Leimert	2017

race_merged.plot.bar(x = 'neighborhood', y='% White', title="Percentage of White Population (2017)")

<matplotlib.axes._subplots.AxesSubplot at 0x7f3dd1c9f9a0>

race_merged.plot.bar(x = 'neighborhood', y='% Black', title="Percentage of Black Population (2017)")

<matplotlib.axes._subplots.AxesSubplot at 0x7f3dd1b7d520>

race_merged.plot.bar(x = 'neighborhood', y='% Latino', title="Percentage of Latino Population (2017)")

<matplotlib.axes._subplots.AxesSubplot at 0x7f3dd1b54dc0>

racefig2017 = px.bar(race_merged, x="neighborhood", y=["White",
 "Black",
 "AmIndian",
 "Native Hawaiian and Other Pacific Islander Alone",
 "Asian", "Some Other Race Alone",'Two or More Races','Hispanic or Latino'], title="Income Breakdown by Neighborhood (2017)", 
       labels={'Geo_NAME':'Census Tract Block Group','value':'Total Households in Atwater Village & Leimert Park', 'variable': 'Household Income'})

racefig2017

Mapping

import geopandas as gpd
block_groups= gpd.read_file('data/tl_2019_06_bg.shp')

# subset to only LA block groups
block_groups_LA = block_groups[block_groups.COUNTYFP == '037']

block_groups_LA.shape

(6425, 13)

columns = list(block_groups)
columns

['STATEFP',
 'COUNTYFP',
 'TRACTCE',
 'BLKGRPCE',
 'GEOID',
 'NAMELSAD',
 'MTFCC',
 'FUNCSTAT',
 'ALAND',
 'AWATER',
 'INTPTLAT',
 'INTPTLON',
 'geometry']

blocks = block_groups_LA[['GEOID','geometry']]
blocks.head()

	GEOID	geometry
30	060371872002	POLYGON ((-118.25704 34.10773, -118.25664 34.1...
31	060371873001	POLYGON ((-118.26544 34.11216, -118.26540 34.1...
32	060371873002	POLYGON ((-118.25924 34.09416, -118.25924 34.0...
36	060375437024	POLYGON ((-118.27553 33.80936, -118.27553 33.8...
37	060375437025	POLYGON ((-118.27549 33.81246, -118.27549 33.8...

blocks.columns = ['FIPS','geometry']

# create a new dataframe based on the join (ATWATER)
blocks_units=blocks.merge(df,on="FIPS")

# subsetting by neighborhood
axA1=blocks_units[blocks_units.neighborhood=='Atwater'].plot(figsize=(12,10),
                 column='p_less_25k',                                  
                  cmap='cool',
                 legend=True,            
                scheme='equal_interval')
# get rid of the axis
axA1.axis('off')
# add a title
axA1.set_title('Census Block Groups in Atwater Village with Annual Household Income Less than $25K (2017)')

Text(0.5, 1.0, 'Census Block Groups in Atwater Village with Annual Household Income Less than $25K (2017)')

# create a new dataframe based on the join (Leimert)
blocks_units2=blocks.merge(df2,on="FIPS")

# subsetting by neighborhood
axL1= blocks_units2[blocks_units2.neighborhood=='Leimert'].plot(figsize=(12,10),
                 column='p_less_25k',                                  
                 cmap='cool',
                 legend=True,
                scheme='equal_interval')
# get rid of the axis
axL1.axis('off')
# add a title
axL1.set_title('Census Block Groups in Leimert Park with Annual Household Income Less than $25K (2017)')

Text(0.5, 1.0, 'Census Block Groups in Leimert Park with Annual Household Income Less than $25K (2017)')

# subsetting by neighborhood
axA2=blocks_units[blocks_units.neighborhood=='Atwater'].plot(figsize=(12,10),
                 column='p_more_100k',                                  
                  cmap='cool',
                 legend=True,
                                
                scheme='equal_interval')

# get rid of the axis
axA2.axis('off')
# add a title
axA2.set_title('Census Block Groups in Atwater Village with Annual Household Income More than $100K (2017)')

Text(0.5, 1.0, 'Census Block Groups in Atwater Village with Annual Household Income More than $100K (2017)')

# subsetting by neighborhood
axL2 = blocks_units2[blocks_units2.neighborhood=='Leimert'].plot(figsize=(12,10),
                 column='p_more_100k',                                  
                  cmap='cool',
                 legend=True,          
                scheme='equal_interval')

# get rid of the axis
axL2.axis('off')

# add a title
axL2.set_title('Census Block Groups in Leimert Park with Annual Household Income More than $100K (2017)')

Text(0.5, 1.0, 'Census Block Groups in Leimert Park with Annual Household Income More than $100K (2017)')

Choropleth Income Map

import folium

Atwater Income Map

m = folium.Map(location=[34.2,-118.2], 
               zoom_start = 9,
               tiles='CartoDB positron', 
               attribution='CartoDB')

# plot chorpleth over the base map
folium.Choropleth(
                  geo_data=blocks_units, # geo data
                  data=blocks_units, # data          
                  key_on='feature.properties.FIPS', # key, or merge column
                  columns=['FIPS', 'p_more_100k'], # [key, value]
                  fill_color='BuPu',
                  line_weight=0.1, 
                  fill_opacity=0.8,
                  line_opacity=0.2, # line opacity (of the border)
                  legend_name='Income X').add_to(m)    # name on the legend color bar
m

Make this Notebook Trusted to load map: File -> Trust Notebook

Leimert Income Map

m2 = folium.Map(location=[34.2,-118.2], 
               zoom_start = 9,
               tiles='CartoDB positron', 
               attribution='CartoDB')

# plot chorpleth over the base map
folium.Choropleth(
                  geo_data=blocks_units2, # geo data
                  data=blocks_units2, # data          
                  key_on='feature.properties.FIPS', # key, or merge column
                  columns=['FIPS', 'p_more_100k'], # [key, value]
                  fill_color='BuPu',
                  line_weight=0.1, 
                  fill_opacity=0.8,
                  line_opacity=0.2, # line opacity (of the border)
                  legend_name='Income X').add_to(m2)    # name on the legend color bar

m2

Make this Notebook Trusted to load map: File -> Trust Notebook

Import ADU Data

import pandas as pd
import plotly.express as px
from sodapy import Socrata
import geopandas as gpd

# connect to the data portal
client = Socrata("data.lacity.org", None)

# First 2000 results, returned as JSON from API / converted to Python list of
# dictionaries by sodapy.
results = client.get("hyem-e7yr", 
                     limit=25000,
                     where = "issue_date between '2017-07-01T00:00:00' and '2020-11-30T00:00:00'",
                     order='issue_date desc')

# Convert to pandas DataFrame
adu = pd.DataFrame.from_records(results)

# print it with .sample, which gives you random rows
adu.head()

WARNING:root:Requests made without an app_token will be subject to strict throttling limits.

	zip_code	address_end	floor_area_l_a_building_code_definition	work_description	reference_old_permit	census_tract	permit_category	latest_status	initiating_office	assessor_parcel	...	address_fraction_end	applicant_address_2	block	location_1	of_residential_dwelling_units	applicant_business_name	unit_range_start	unit_range_end	occupancy	suffix_direction
0	90047	10208	111	ePlan Adu. CONVERT EXISTING DETACHED GARAGE IN...	20ON 2243	2380.00	Plan Check	Issued	METRO	012	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	90008	3832	0	Convert (E) detached garage to ADU per CA Govt...	20ON 2233	2342.00	Plan Check	Issued	METRO	012	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	90016	5454	1245.5	NEW TWO STORY ADU PER ORDINANCE WITH ATTACHED...	20ON 2236	2199.01	Plan Check	Issued	WEST LA	011	...	1/2	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	90065	1000	0	Grading for (N) ADU Conversion and (N) office/...	20ON 2238	1862.03	Plan Check	Issued	METRO	030	...	NaN	A	25	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	90064	2833	545	CONVERT (E) DETACHED GARAGE INTO ACCESSORY DWE...	20ON 2233	2713.00	Plan Check	Issued	WEST LA	006	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

5 rows × 53 columns

adu.shape

(2096, 53)

list(adu)

['zip_code',
 'address_end',
 'floor_area_l_a_building_code_definition',
 'work_description',
 'reference_old_permit',
 'census_tract',
 'permit_category',
 'latest_status',
 'initiating_office',
 'assessor_parcel',
 'applicant_first_name',
 'zone',
 'assessor_book',
 'applicant_address_1',
 'license_expiration_date',
 'license_type',
 'valuation',
 'pcis_permit',
 'applicant_relationship',
 'address_start',
 'street_name',
 'street_suffix',
 'street_direction',
 'applicant_address_3',
 'status_date',
 'applicant_last_name',
 'license',
 'floor_area_l_a_zoning_code_definition',
 'issue_date',
 'assessor_page',
 'contractors_business_name',
 'tract',
 'lot',
 'permit_sub_type',
 'of_stories',
 'permit_type',
 'principal_first_name',
 'contractor_state',
 'principal_middle_name',
 'contractor_city',
 'principal_last_name',
 'contractor_address',
 'address_fraction_start',
 'address_fraction_end',
 'applicant_address_2',
 'block',
 'location_1',
 'of_residential_dwelling_units',
 'applicant_business_name',
 'unit_range_start',
 'unit_range_end',
 'occupancy',
 'suffix_direction']

adu = gpd.read_file('https://data.lacity.org/resource/hyem-e7yr.geojson')

adu.head()

	assessor_parcel	zip_code	location_1_address	work_description	:@computed_region_2dna_qi2s	applicant_address_3	floor_area_l_a_zoning_code_definition	address_fraction_end	project_number	suffix_direction	...	event_code	reference_old_permit	applicant_relationship	:@computed_region_k96s_3jcv	contractor_state	license_expiration_date	:@computed_region_qz3q_ghft	applicant_address_2	permit_sub_type	geometry
0	034	91367	None	NEW FIRE SPRINKLER SYSTEM FOR ADU PER NFPA 13...	None	ARLETA, CA	None	None	None	None	...	None	None	Contractor	None	CA	2021-06-30T00:00:00	None	None	1 or 2 Family Dwelling	None
1	046	91316	None	NFPA13D FOR ADU. EXISTING 1'' DOMESTIC WATER ...	None	SUN VALLEY, CA	None	None	None	None	...	None	None	Contractor	None	CA	2021-12-31T00:00:00	None	UNIT G	1 or 2 Family Dwelling	None
2	024	90025	None	NFPA 13D SYSTEM . 1" DOMESTIC METER SEVRVES TH...	None	None	None	None	None	None	...	None	None	Contractor	None	CA	2021-10-31T00:00:00	None	None	1 or 2 Family Dwelling	None
3	014	90034	None	NEW FIRE SPRINKLER SYSTEM FOR PER NFPA 13D FOR...	None	WOODLAND HILLS, CA	None	None	None	None	...	None	None	Contractor	None	CA	2021-01-31T00:00:00	None	None	1 or 2 Family Dwelling	None
4	027	91436		New fire sprinkler system for ADU per NFPA-13D...	62	WEST HILLS,CA	None	None	None	None	...	None	None	Agent for Contractor	327	CA	2021-10-31T00:00:00	19737	None	1 or 2 Family Dwelling	POINT (-118.49822 34.14598)

5 rows × 65 columns

adu = adu[['issue_date','geometry']]

# print it with .sample, which gives you random rows
adu.head()

	issue_date	geometry
0	2020-12-03T00:00:00	None
1	2020-10-30T00:00:00	None
2	2020-10-27T00:00:00	None
3	2020-09-30T00:00:00	None
4	2020-09-18T00:00:00	POINT (-118.49822 34.14598)

#We can a latitude and longtitude column so that we can map it
adu['x'] = adu.geometry.x
adu['y'] = adu.geometry.y

adu = adu.dropna()

# get the layers into a web mercator projection
# reproject to web mercator
adu = adu.to_crs('EPSG:3857')

# map it!
adu.plot(figsize=(12,12),color='teal')

<matplotlib.axes._subplots.AxesSubplot at 0x7f3dbcceaac0>

adu = adu.to_crs('EPSG:3857')

blocks_units= blocks_units.to_crs('EPSG:3857')

# subset the neighborhoods geodataframe for a single neighborhood
neighborhood = blocks_units[blocks_units.neighborhood=='Atwater']


# use the bounding box coordinates to set the x and y limits
minx, miny, maxx, maxy = neighborhood.geometry.total_bounds

# do a spatial join to get crime in neighborhood
adus_in_neighborhood = gpd.sjoin(adu,neighborhood,how='inner')

# define the base layer to be the neighborhood polygon
base = neighborhood.plot(figsize=(12,12),
                         color='red', 
                         edgecolor='red',
                         alpha=0.1)

# add the crime data, making sure to add the neighborhood polygon
ax = adus_in_neighborhood.plot(ax=base, 
#                                 column='applicant_address_3', 
                                markersize=40, 
                                legend=True,
                                cmap='tab20',
                                legend_kwds={
                                   'loc': 'upper right',
                                   'bbox_to_anchor':(1.3,1)
                                }                  # this puts the legend to the side
                            )

# set the map extent to the extent of the neighborhood bounds
ax.set_xlim(minx - 200, maxx + 200) # added/substracted value is to give some margin around total bounds
ax.set_ylim(miny - 200, maxy + 200)

# turn off the axis
ax.axis('off')

# add a title
ax.set_title('ADUs Constructed in Los Angeles',fontsize=20)

# add a basemap
ctx.add_basemap(m2,source=ctx.providers.CartoDB.Positron)
ax

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-198-dc71909b008f> in <module>
     38 
     39 # add a basemap
---> 40 ctx.add_basemap(m2,source=ctx.providers.CartoDB.Positron)
     41 ax

NameError: name 'ctx' is not defined

neighborhood.crs